Investigating a flower-insect forager network in a mountain grassland community using pollen DNA barcoding

Faced with the decline of pollinators, it is relevant to strengthen our understanding of the whole plant-pollinator web in semi-natural grasslands that serve as refuges for pollinator populations. The aim of this study was to explore the diversity of flower-foraging insects involved in pollen transfer in mountain semi-natural grasslands. Insects actively collecting pollen and/or nectar were caught in spring in six mountain semi-natural grasslands displaying a floristic richness gradient. Individual determinations of insects were made at the finest possible taxonomic scale and pollen loads were removed from the insect body. Using next-generation DNA sequencing, pollens were identified through the ribosomal DNA cistron using the ITS2 database and the ITS plant rDNA cistron sequences from Genbank. A total of 236 flower-foraging insects were collected. Diptera represented 82% of the total catches distantly followed by Hymenoptera (15%) and Apoidea (bees) (11%). Visual observations revealed that Diptera foraged on 16 of the 21 flower species visited by insects. DNA metabarcoding showed that 82% (191) of all of the collected insects were carrying pollen and 44% (104) were carrying two genera of plants or more. Our results demonstrate that Diptera are potential key-pollinators in mountain semi-natural grasslands that cannot be overlooked by the scientific community. However difficulties of taxonomic determination due to severe shortage of experts for Diptera have to be urgently overcome. Further studies on the link between pollen transfer and actual pollination in a global change context are also required. Moreover, our results support the idea that DNA metabarcoding provides accurate information about the plants-insects networks but it also pointed out sensitive issues, especially the necessity to build reliable national barcode databases.     

Pectinase production by <Emphasis Type="Italic">Aspergillus giganteus</Emphasis> in solid-state fermentation: optimization,scale-up,biochemical characterization and its application in olive-oil extraction

The application of pectinases in industrial olive-oil processes is restricted by its production cost. Consequently, new fungal strains able to produce higher pectinase titers are required. The aim of this work was to study the capability of Aspergillus giganteus NRRL10 to produce pectinolytic enzymes by SSF and evaluate the application of these in olive-oil extraction. A. giganteus was selected among 12 strains on the basis of high pectinolytic activity and stability. A mixture composed by wheat bran, orange, and lemon peels was selected as the best substrate for enzyme production. Statistical analyses of the experimental design indicated that pH, temperature, and CaCl₂ are the main factors that affect the production. Subsequently, different aeration flows were tested in a tray reactor; the highest activity was achieved at 20 L min^?1 per kilogram of dry substrate (kgds). Finally, the pectinolytic enzymes from A. giganteus improved the oil yield and rheological characteristics without affecting oil chemical properties.     

Practical application of genomic selection in a doubled-haploid winter wheat breeding program

Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.